Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine
Aiming at the Gear vibration signals have the nonlinear and non-stationary characteristics,to avoid the disadvantages of traditional time and frequency domain method in the characterization of the state of equipment and failure identification model "less learning"problem caused by small sa...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2016-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.06.028 |
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| _version_ | 1841547910038159360 |
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| author | Qin Bo Yang Yunzhong Chen Min Guo Wei Liu Yongliang Wang Jianguo |
| author_facet | Qin Bo Yang Yunzhong Chen Min Guo Wei Liu Yongliang Wang Jianguo |
| author_sort | Qin Bo |
| collection | DOAJ |
| description | Aiming at the Gear vibration signals have the nonlinear and non-stationary characteristics,to avoid the disadvantages of traditional time and frequency domain method in the characterization of the state of equipment and failure identification model "less learning"problem caused by small sample size,the gearbox fault diagnosis method based on kurtosis and intrinsic mode function（ IMF） energy feature and least squares support vector machine（ LS-SVM） is proposed. Firstly,by using ensemble empirical mode decomposition（ EEMD）,the collected gear vibration signal is decomposed,on this basis,the IMF components which contain major fault information are extracted and its energy feature and kurtosis are calculated,and the time-frequency domain two kinds of the feature vector are constructed. Secondly,taking the fusion feature vectors of three conditions of normal,the root crack and broken as input,the gearbox fault type identification is conducted based on the LS-SVM. The experiment results show that the gear working state can be accurately identified by this method. It has higher efficiency of fault identification compared with the BP neural network and SVM model and a new way for the gear fault diagnosis is provided. |
| format | Article |
| id | doaj-art-4639c84a85e94b5aa3141e877e0b0330 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-4639c84a85e94b5aa3141e877e0b03302025-01-10T14:17:14ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392016-01-014012613129924447Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector MachineQin BoYang YunzhongChen MinGuo WeiLiu YongliangWang JianguoAiming at the Gear vibration signals have the nonlinear and non-stationary characteristics,to avoid the disadvantages of traditional time and frequency domain method in the characterization of the state of equipment and failure identification model "less learning"problem caused by small sample size,the gearbox fault diagnosis method based on kurtosis and intrinsic mode function（ IMF） energy feature and least squares support vector machine（ LS-SVM） is proposed. Firstly,by using ensemble empirical mode decomposition（ EEMD）,the collected gear vibration signal is decomposed,on this basis,the IMF components which contain major fault information are extracted and its energy feature and kurtosis are calculated,and the time-frequency domain two kinds of the feature vector are constructed. Secondly,taking the fusion feature vectors of three conditions of normal,the root crack and broken as input,the gearbox fault type identification is conducted based on the LS-SVM. The experiment results show that the gear working state can be accurately identified by this method. It has higher efficiency of fault identification compared with the BP neural network and SVM model and a new way for the gear fault diagnosis is provided.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.06.028IMF energyKurtosisLeast squares support vector machineGearFault diagnosis |
| spellingShingle | Qin Bo Yang Yunzhong Chen Min Guo Wei Liu Yongliang Wang Jianguo Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine Jixie chuandong IMF energy Kurtosis Least squares support vector machine Gear Fault diagnosis |
| title | Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine |
| title_full | Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine |
| title_fullStr | Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine |
| title_full_unstemmed | Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine |
| title_short | Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine |
| title_sort | fault diagnosis approach of gear based on two features and least squares support vector machine |
| topic | IMF energy Kurtosis Least squares support vector machine Gear Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.06.028 |
| work_keys_str_mv | AT qinbo faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine AT yangyunzhong faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine AT chenmin faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine AT guowei faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine AT liuyongliang faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine AT wangjianguo faultdiagnosisapproachofgearbasedontwofeaturesandleastsquaressupportvectormachine |